All ETDs from UAB

Advisory Committee Chair

Virginia P Sisiopiku

Advisory Committee Members

Andrew Sullivan

Abidin Yildirim

Document Type

Thesis

Date of Award

2020

Degree Name by School

Master of Science in Civil Engineering (MSCE) School of Engineering

Abstract

Traffic control signals are used to assign the right-of-way to traffic movements at intersections. By doing so, they facilitate the orderly movement of vehicles and pedestrians, improve the traffic-handling capacity of intersections and reduce the number of conflicts between movements, thus improving traffic safety. The most widely implemented method of traffic control is the fixed-time signals. Fixed-time traffic controllers use a simple electric mechanism that allow the signals to change at predefined time intervals; however, these simple mechanisms were not able to adjust to changes in traffic demand during the day. To address this issue, contemporary traffic signal controllers are available (i.e., actuated and adaptive systems) that rely on point detection methods such as in-pavement loop or video detectors to collect traffic demand information and use the information to change change signal timings in response of changes in demand. Still, these point detectors cause many technical and implementation issues as well as they can provide only limited vehicle information at a fixed location. In the last decade, new data collection technologies are emerging which provide opportunities for improving signal timing, optimization and management. In particular, the emergence of Connected Vehicle (CV) technology holds great promise. CV technology allows wireless communication between a vehicle and its surroundings, namely Vehicle to Everything (V2X) communication. Via this wireless communication, vehicle positions, speed, and acceleration status can be collected and used to optimize traffic signal timing. Recently some studies have been completed using CV collected data for traffic signal optimization problem considered a Nondeterministic Polynomial Time complete (NP-complete) approach. However, this approach potentially undermines their implementation because of the computational complexities. The objective of this thesis was twofold: a) Document the current state of practice related to traffic signal operation, optimization, and management practices in the Southeast United States; and b) propose an alternative approach to the NP-complete method for utilizing CV data for traffic signal timing and coordination and demonstrate its feasibility and value. In meeting the objectives of the thesis, this study first developed and conducted a comprehensive survey of traffic signal operation, optimization, and management practices in the Southeast United States. Twenty representatives of transportation agencies that operate various-sized traffic signal systems in six states in the Southeast responded to the survey. The resposes were aggregated by agency size and used to document the state-of-practice. Also, current barriers were identified, including limitations in available resources (such as funding and staffing levels), and lack of efficient trigger data for retiming signals. The analysis of the survey results highlighted opportunities for refining and improving current practices through the use of emerging data collection and modeling options. In response to the survey findings and given the gaps in the literature, the study proposed a new approach, namely the Prediction Based Conditional (PCO) approach, which utilizes CV gathered data for real-time traffic signal optimization and coordination. The performance of the proposed approach was compared with a fixed time signal control method optimized by HCS7 in the Vissim microscopic simulation tool (version 2020.00-06, 64-bit). The study results illustrated that the proposed PCO approach can reduce average number of queued vehicles by 33.3%, average delay (sec/veh) by 13%, and the number of stops by 18.6% in comparison to HCS7 optimized fixed time traffic signal control. The findings from these study are expected to be valuable to transportation agencies, engineers, and researchers interested in improving signal timing optimization practices through the use of emerging technologies.

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